CODIS: A New Compression Scheme for Bitmap Indexes

2017 ◽  
Author(s):  
Wenxun Zheng ◽  
Yin Liu ◽  
Zhen Chen ◽  
Junwei Cao
Keyword(s):  
Compression Scheme ◽  
Bitmap Indexes

APPLE: a new compression scheme for bitmap indexes

2020 ◽  
Author(s):  
Ge Ma ◽  
Tao Yu ◽  
Guowei Zhu ◽  
Kan Lv ◽  
Qiyang Huang ◽  
...  
Keyword(s):  
Compression Scheme ◽  
Bitmap Indexes

A Test Data Compression Scheme Based on Mixed Fixed and Variable Length Coding

2009 ◽  
Vol 31 (10) ◽  
pp. 1826-1834 ◽  
Author(s):  
Wen-Fa ZHAN ◽  
Hua-Guo LIANG ◽  
Feng SHI ◽  
Zheng-Feng HUANG
Keyword(s):  
Data Compression ◽  
Test Data ◽  
Variable Length ◽  
Test Data Compression ◽  
Compression Scheme ◽  
Variable Length Coding

A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

2020 ◽  
Author(s):  
S. Poonguzhali ◽  
Avinash Sharma ◽  
V. Vedanarayanan ◽  
A. Aranganathan ◽  
T. Gomathi ◽  
...  
Keyword(s):  
Data Hiding ◽  
Image Inpainting ◽  
Compression Scheme

scholarly journals A data reduction and compression description for high throughput time-resolved electron microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abhik Datta ◽  
Kian Fong Ng ◽  
Deepan Balakrishnan ◽  
Melissa Ding ◽  
See Wee Chee ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Temporal Resolution ◽  
Data Reduction ◽  
Accurate Estimation ◽  
Data Streaming ◽  
Compression Scheme ◽  
Raw Data ◽  
Time Resolved ◽  
Detection And Counting ◽  
Spatio Temporal

AbstractFast, direct electron detectors have significantly improved the spatio-temporal resolution of electron microscopy movies. Preserving both spatial and temporal resolution in extended observations, however, requires storing prohibitively large amounts of data. Here, we describe an efficient and flexible data reduction and compression scheme (ReCoDe) that retains both spatial and temporal resolution by preserving individual electron events. Running ReCoDe on a workstation we demonstrate on-the-fly reduction and compression of raw data streaming off a detector at 3 GB/s, for hours of uninterrupted data collection. The output was 100-fold smaller than the raw data and saved directly onto network-attached storage drives over a 10 GbE connection. We discuss calibration techniques that support electron detection and counting (e.g., estimate electron backscattering rates, false positive rates, and data compressibility), and novel data analysis methods enabled by ReCoDe (e.g., recalibration of data post acquisition, and accurate estimation of coincidence loss).

scholarly journals Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module

2021 ◽  
Vol 11 (10) ◽  
pp. 4614
Author(s):  
Xiaofei Chao ◽  
Xiao Hu ◽  
Jingze Feng ◽  
Zhao Zhang ◽  
Meili Wang ◽  
...  
Keyword(s):  
Disease Control ◽  
Classification Accuracy ◽  
Mobile Terminal ◽  
Disease Classification ◽  
Memory Usage ◽  
Compression Scheme ◽  
Accurate Identification ◽  
Average Classification Accuracy ◽  
Network Compression ◽  
Control And Management

The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.

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